ANALYZING THE IMPACT OF LEADERSHIP STYLES ON EMPLOYEE SATISFACTION IN THE AUTOMOBILE INDUSTRY USING NEURAL NETWORK MODELING: AN EMPIRICAL APPROACH

Abstract

This study explores the influence of various leadership styles on employee satisfaction within the automobile industry using the Neural Network (NN) modeling approach. The research aims to identify the most significant leadership traits that contribute to overall employee satisfaction by applying advanced predictive analytics. Data was collected through structured questionnaires administered to employees across multiple automobile firms. The neural network model was trained to evaluate complex, non-linear relationships between leadership dimensions and satisfaction indicators. The results reveal key leadership components—such as transformational leadership, participative decision-making, and communication transparency—that significantly enhance employee satisfaction. The study provides actionable insights for HR managers and organizational leaders in the automobile sector, emphasizing the importance of data-driven strategies for leadership development and employee retention.